From semiparametric theory[1]}, there is a
one-to-one correspondence between influence functions and
estimators. Thus, if the form of influence functions in a
specific model involving a parameter \(\beta \)  can be derived,
estimating equations leading to estimators for \(\beta \)  can be
developed. As we demonstrate in Section ,
influence functions corresponding to estimators for \(\beta \)  based
on the data available at an interim analysis can be derived from
the influence function (REF ), and the resulting
estimators exploit baseline and time-dependent covariate
information to gain precision.
